Field of the Invention
The invention concerns a method for determining a deviation of diffusion-weighted magnetic resonance image data of an examination object from normal diffusion behavior. The invention also concerns a method for producing an anomaly map or a normality map of a field of a view of an examination object. The invention also concerns a controller for a magnetic resonance system, a magnetic resonance system, and a non-transitory, computer-readable storage medium encoded with programming instructions, for implementing such methods.
Description of the Prior Art
“Magnetic resonance scans” as used herein mean data generated from raw data acquired from the inside of the examination object with the use of a magnetic resonance scanner controlled within the scope of the method, as well as parameter maps that reproduce a spatial or temporal distribution of specific parameter values inside the examination object, and that can be generated from the raw data.
Diffusion-weighted magnetic resonance scans are magnetic resonance scans with which the diffusion movement of certain substances, in particular water molecules in the body tissue, can be scanned and be displayed in a spatially resolved manner. Diffusion imaging has become established in everyday clinical practice, in particular for diagnosing strokes, since the affected regions of the brain can already be seen much earlier in diffusion-weighted images than in conventional magnetic resonance scans. In addition, diffusion imaging is increasingly being used in the fields of oncological, cardiological and musculoskeletal diseases. One variant of diffusion-weighted magnetic resonance tomography is diffusion tensor imaging, in which the anisotropy of the diffusion is also detected. Diffusion-weighted magnetic resonance scans as used herein encompass magnetic resonance scans generated in the course of diffusion-weighted magnetic resonance tomography as well as magnetic resonance scans generated in the course of diffusion tensor imaging.
Diffusion-encoded raw data must first be acquired for generating diffusion-weighted magnetic resonance images. This is done using specific scanning sequences, which will hereinafter be called diffusion gradient scanning sequences. A characteristic of these scanning sequences is that after conventional tilting (flipping) of the spins into a plane perpendicular to the basic magnetic field of the magnetic resonance scanner, a specific sequence of gradient magnetic field pulses is switched that change the field strength of the scanner's magnetic field in a predefined direction. Where there is a diffusion movement, the precessing nuclei come out of phase, and this can be perceived in the scanning signal.
With diffusion imaging, a number of images having different diffusion directions and weightings, i.e. having different diffusion-encoding gradient pulses, are usually recorded and combined with each other. The strength of the diffusion weighting is usually defined by what is known as the diffusion weighting factor, also called the “b-value”. The different diffusion images or the images combined therefrom, or parameter maps, can then be used for the desired diagnostic purposes. To be able to correctly estimate the effect of the diffusion movement, a further reference scan is used in many cases for comparison, in which no diffusion-encoding gradient pulse is activated, i.e. an image where b=0. The pulse-scanning sequence for acquisition of the reference raw data is constructed in the same way as the diffusion gradient scanning sequence, with the exception of transmission of the diffusion-encoding gradient pulses. Alternatively, a reference scan can be carried out with a b-value< >0.
Usually images or parameter maps are used in MR diffusion imaging for diagnosis, in which a free diffusion process, also called a free normal Gaussian diffusion process, having an apparent diffusion coefficient (ADC=apparent diffusion coefficient) is assumed. This process is characterized by the signal strength decreasing according to an exponential correlation as a function of the diffusion-weighting factor.
Extensions to this model take into account, for example, the anisotropy of diffusion in microscopically limited geometries: water molecules, for example, can move faster along nerve fibers than perpendicular thereto. The diffusion tensor model always detects these correlations under the assumption of an accordingly direction-dependent, free normal Gaussian diffusion process and allows the calculation and display of associated parameters or parameter values, such as, for example, parameters relating to directional anisotropy.
Furthermore, there is a range of further approaches with which deviations from the Gaussian behavior can be described with corresponding model functions. These include, for example, the IVIM-model (IVIM=Intra-Voxel Incoherent Motion) in which a bi-exponential decrease in the signal amplitude is assumed as a function of the b-value, due to perfusion effects. The Kurtosis model, in which deviations of the exponential dependency of the signal strength from the b-value are modeled with tensors of a higher order, also belong to this category of approaches.
Detection of a large number of diffusion directions and/or weightings enables a more accurate image of the local diffusion geometry to be obtained. A number of preferred directions can therefore be resolved within one image voxel with HARDI (High Angular Resolution Diffusion Imaging=diffusion imaging with high angular resolution), DSI (Diffusion Spectrum Imaging) or Q-Ball-methods (see David S. Tuch, “Q-Ball Imaging”, Magnetic Resonance in Medicine 52:1358-1372 (2004)).
In addition, methods are known with which the dependency of the signal intensity is taken into account in the experiment not only by the b-value and the direction, but also by specific interval durations in order to draw conclusions about microscopic tissue parameters (e.g. the axon radius, the surface-to-volume ratios, etc.) using model assumptions.
The last-mentioned group of methods offers the possibility of generating new contrasts, based on the diffusion, having a potentially high clinical value. However, the assumptions underlying the models are usually highly simplified and the “parameter maps” based thereon dubious in respect of their validity.